An act, process, or methodology of making something (such as a design, system, or decision) as fully perfect, functional, or effective as possible. – Merriam-Webster
Code optimization is the process of enhancing code quality and efficiency.
“Make it work, then make it beautiful, then if you really, really have to, make it fast. 90 percent of the time, if you make it beautiful, it will already be fast. So really, just make it beautiful!” –Joe Armstrong
Optimization should be the final step of your programming practice. Performance can be high following the pre-optimization steps. Only optimize when it’s necessary!
Goal: build a model using the insurance claims of ~25 million lives with two years’ worth of data (i.e., billions of records!) to estimate the cost of a procedure.
Steps:
Took months to complete…
…Other solutions are also possible!
# R Studio Server: 420ms
# R Studio Desktop Below
library(profvis)
library(git2r)
profvis({
git_object <-
function(data_object = NULL) {
object_names <- sort(unique(subset(odb_blobs(), grepl(".rda", name))$name))
git_obj <- grep(data_object, object_names, value = TRUE, ignore.case = TRUE)
return(git_obj)
}
git_object("pkg")
})… using base R system2() produces much faster results than git2r’s odb_blobs(). Sometimes the newer thing out there isn’t always the best option!
profvis and profile.microbenchmark and bench.snow and parallel.ff, bigmemory, and feather.gc() (garbage collect) to release memory (not required, but doesn’t hurt to use after removing large objects).data.table for faster computations.“The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places and at the wrong times; premature optimization is the root of all evil (or at least most of it) in programming.”
- Donald Knuth, Computer Programming as an Art
TIME!